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Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale

Author

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  • Marius Arend

    (University of Potsdam
    Max Planck Institute of Molecular Plant Physiology
    Center of Plant Systems Biology and Biotechnology)

  • David Zimmer

    (Computational Systems Biology, TU Kaiserslautern)

  • Rudan Xu

    (University of Potsdam
    Max Planck Institute of Molecular Plant Physiology)

  • Frederik Sommer

    (Molecular Biotechnology & Systems Biology, TU Kaiserslautern)

  • Timo Mühlhaus

    (Computational Systems Biology, TU Kaiserslautern)

  • Zoran Nikoloski

    (University of Potsdam
    Max Planck Institute of Molecular Plant Physiology
    Center of Plant Systems Biology and Biotechnology)

Abstract

Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.

Suggested Citation

  • Marius Arend & David Zimmer & Rudan Xu & Frederik Sommer & Timo Mühlhaus & Zoran Nikoloski, 2023. "Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40498-1
    DOI: 10.1038/s41467-023-40498-1
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    as
    1. Iván Domenzain & Benjamín Sánchez & Mihail Anton & Eduard J. Kerkhoven & Aarón Millán-Oropeza & Céline Henry & Verena Siewers & John P. Morrissey & Nikolaus Sonnenschein & Jens Nielsen, 2022. "Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Philipp Wendering & Marius Arend & Zahra Razaghi-Moghadam & Zoran Nikoloski, 2023. "Data integration across conditions improves turnover number estimates and metabolic predictions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Hongzhong Lu & Feiran Li & Benjamín J. Sánchez & Zhengming Zhu & Gang Li & Iván Domenzain & Simonas Marcišauskas & Petre Mihail Anton & Dimitra Lappa & Christian Lieven & Moritz Emanuel Beber & Nikola, 2019. "A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
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